Summary of Establishing Task Scaling Laws Via Compute-efficient Model Ladders, by Akshita Bhagia et al.
Establishing Task Scaling Laws via Compute-Efficient Model Ladders
by Akshita Bhagia, Jiacheng Liu, Alexander Wettig, David Heineman, Oyvind Tafjord, Ananya Harsh Jha, Luca Soldaini, Noah A. Smith, Dirk Groeneveld, Pang Wei Koh, Jesse Dodge, Hannaneh Hajishirzi
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper develops novel methods for predicting the performance of pre-trained language models on specific tasks, which is crucial for efficient model training and development. The authors introduce “task scaling laws” and “model ladders” to predict individual task performance in the overtrained setting, addressing limitations of standard power laws for language modeling loss. A two-step prediction approach is used, first predicting task-specific loss based on model and data size, then using this loss to estimate task performance. The authors train a set of small-scale ladder models, collect data points to fit parameterized functions, and make predictions for two target models. Predictions are accurate within 2 points of absolute error on multiple-choice tasks written in ranked classification format. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how pre-trained language models can be used on specific tasks. The authors develop a way to predict how well these models will do on certain tasks, which is important because it would save time and computer power if we could figure out which models to use for each task without having to train them all ourselves. They also find that some tasks are harder to predict than others and that using fewer ladder models can actually make predictions worse. |
Keywords
» Artificial intelligence » Classification » Scaling laws
